Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory120.0 B

Variable types

Numeric11
Categorical4

Alerts

Country is highly overall correlated with RegionHigh correlation
Crop_Yield_MT_per_HA is highly overall correlated with Economic_Impact_Million_USDHigh correlation
Economic_Impact_Million_USD is highly overall correlated with Crop_Yield_MT_per_HAHigh correlation
Region is highly overall correlated with CountryHigh correlation
Extreme_Weather_Events has 899 (9.0%) zeros Zeros

Reproduction

Analysis started2024-10-19 18:25:50.481889
Analysis finished2024-10-19 18:26:33.486987
Duration43.01 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Year
Real number (ℝ)

Distinct35
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.0887
Minimum1990
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-10-19T18:26:33.653285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1990
5-th percentile1991
Q11999
median2007
Q32016
95-th percentile2023
Maximum2024
Range34
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.084245
Coefficient of variation (CV)0.0050243147
Kurtosis-1.2069501
Mean2007.0887
Median Absolute Deviation (MAD)9
Skewness-0.0067932823
Sum20070887
Variance101.692
MonotonicityNot monotonic
2024-10-19T18:26:34.036608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1999 335
 
3.4%
2019 317
 
3.2%
1991 314
 
3.1%
2012 313
 
3.1%
2004 307
 
3.1%
2013 305
 
3.0%
1994 305
 
3.0%
2001 300
 
3.0%
1996 295
 
2.9%
2023 294
 
2.9%
Other values (25) 6915
69.2%
ValueCountFrequency (%)
1990 250
2.5%
1991 314
3.1%
1992 274
2.7%
1993 257
2.6%
1994 305
3.0%
1995 277
2.8%
1996 295
2.9%
1997 287
2.9%
1998 239
2.4%
1999 335
3.4%
ValueCountFrequency (%)
2024 281
2.8%
2023 294
2.9%
2022 288
2.9%
2021 292
2.9%
2020 278
2.8%
2019 317
3.2%
2018 272
2.7%
2017 275
2.8%
2016 293
2.9%
2015 294
2.9%

Country
Categorical

High correlation 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
USA
1032 
Australia
1032 
China
1031 
Nigeria
1029 
India
1025 
Other values (5)
4851 

Length

Max length9
Median length7
Mean length6.1925
Min length3

Characters and Unicode

Total characters61925
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndia
2nd rowChina
3rd rowFrance
4th rowCanada
5th rowIndia

Common Values

ValueCountFrequency (%)
USA 1032
10.3%
Australia 1032
10.3%
China 1031
10.3%
Nigeria 1029
10.3%
India 1025
10.2%
Canada 984
9.8%
Argentina 984
9.8%
France 978
9.8%
Russia 961
9.6%
Brazil 944
9.4%

Length

2024-10-19T18:26:34.348883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-19T18:26:34.686949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
usa 1032
10.3%
australia 1032
10.3%
china 1031
10.3%
nigeria 1029
10.3%
india 1025
10.2%
canada 984
9.8%
argentina 984
9.8%
france 978
9.8%
russia 961
9.6%
brazil 944
9.4%

Most occurring characters

ValueCountFrequency (%)
a 11968
19.3%
i 8035
13.0%
n 5986
 
9.7%
r 4967
 
8.0%
A 3048
 
4.9%
e 2991
 
4.8%
s 2954
 
4.8%
t 2016
 
3.3%
C 2015
 
3.3%
g 2013
 
3.3%
Other values (13) 15932
25.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61925
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11968
19.3%
i 8035
13.0%
n 5986
 
9.7%
r 4967
 
8.0%
A 3048
 
4.9%
e 2991
 
4.8%
s 2954
 
4.8%
t 2016
 
3.3%
C 2015
 
3.3%
g 2013
 
3.3%
Other values (13) 15932
25.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61925
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11968
19.3%
i 8035
13.0%
n 5986
 
9.7%
r 4967
 
8.0%
A 3048
 
4.9%
e 2991
 
4.8%
s 2954
 
4.8%
t 2016
 
3.3%
C 2015
 
3.3%
g 2013
 
3.3%
Other values (13) 15932
25.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61925
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11968
19.3%
i 8035
13.0%
n 5986
 
9.7%
r 4967
 
8.0%
A 3048
 
4.9%
e 2991
 
4.8%
s 2954
 
4.8%
t 2016
 
3.3%
C 2015
 
3.3%
g 2013
 
3.3%
Other values (13) 15932
25.7%

Region
Categorical

High correlation 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
South
754 
Northeast
752 
North
 
524
Central
 
466
Punjab
 
288
Other values (29)
7216 

Length

Max length26
Median length16
Mean length9.2447
Min length4

Characters and Unicode

Total characters92447
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWest Bengal
2nd rowNorth
3rd rowIle-de-France
4th rowPrairies
5th rowTamil Nadu

Common Values

ValueCountFrequency (%)
South 754
 
7.5%
Northeast 752
 
7.5%
North 524
 
5.2%
Central 466
 
4.7%
Punjab 288
 
2.9%
Victoria 283
 
2.8%
New South Wales 276
 
2.8%
East 273
 
2.7%
South West 270
 
2.7%
Ontario 269
 
2.7%
Other values (24) 5845
58.5%

Length

2024-10-19T18:26:35.032903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
south 1560
 
12.0%
west 1058
 
8.1%
north 1023
 
7.8%
northeast 752
 
5.8%
central 701
 
5.4%
east 533
 
4.1%
punjab 288
 
2.2%
victoria 283
 
2.2%
new 276
 
2.1%
wales 276
 
2.1%
Other values (26) 6299
48.3%

Most occurring characters

ValueCountFrequency (%)
t 10464
 
11.3%
e 8806
 
9.5%
a 8592
 
9.3%
r 6547
 
7.1%
o 6056
 
6.6%
s 5678
 
6.1%
h 4712
 
5.1%
i 4014
 
4.3%
u 4001
 
4.3%
n 3951
 
4.3%
Other values (31) 29626
32.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 92447
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 10464
 
11.3%
e 8806
 
9.5%
a 8592
 
9.3%
r 6547
 
7.1%
o 6056
 
6.6%
s 5678
 
6.1%
h 4712
 
5.1%
i 4014
 
4.3%
u 4001
 
4.3%
n 3951
 
4.3%
Other values (31) 29626
32.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 92447
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 10464
 
11.3%
e 8806
 
9.5%
a 8592
 
9.3%
r 6547
 
7.1%
o 6056
 
6.6%
s 5678
 
6.1%
h 4712
 
5.1%
i 4014
 
4.3%
u 4001
 
4.3%
n 3951
 
4.3%
Other values (31) 29626
32.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 92447
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 10464
 
11.3%
e 8806
 
9.5%
a 8592
 
9.3%
r 6547
 
7.1%
o 6056
 
6.6%
s 5678
 
6.1%
h 4712
 
5.1%
i 4014
 
4.3%
u 4001
 
4.3%
n 3951
 
4.3%
Other values (31) 29626
32.0%

Crop_Type
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Wheat
1047 
Cotton
1044 
Vegetables
1036 
Corn
1022 
Rice
1022 
Other values (5)
4829 

Length

Max length10
Median length9
Mean length6.391
Min length4

Characters and Unicode

Total characters63910
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCorn
2nd rowCorn
3rd rowWheat
4th rowCoffee
5th rowSugarcane

Common Values

ValueCountFrequency (%)
Wheat 1047
10.5%
Cotton 1044
10.4%
Vegetables 1036
10.4%
Corn 1022
10.2%
Rice 1022
10.2%
Sugarcane 995
10.0%
Fruits 979
9.8%
Soybeans 958
9.6%
Barley 952
9.5%
Coffee 945
9.4%

Length

2024-10-19T18:26:35.310945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-19T18:26:35.648187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
wheat 1047
10.5%
cotton 1044
10.4%
vegetables 1036
10.4%
corn 1022
10.2%
rice 1022
10.2%
sugarcane 995
10.0%
fruits 979
9.8%
soybeans 958
9.6%
barley 952
9.5%
coffee 945
9.4%

Most occurring characters

ValueCountFrequency (%)
e 9972
15.6%
a 5983
 
9.4%
t 5150
 
8.1%
o 5013
 
7.8%
n 4019
 
6.3%
r 3948
 
6.2%
C 3011
 
4.7%
s 2973
 
4.7%
g 2031
 
3.2%
c 2017
 
3.2%
Other values (13) 19793
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63910
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 9972
15.6%
a 5983
 
9.4%
t 5150
 
8.1%
o 5013
 
7.8%
n 4019
 
6.3%
r 3948
 
6.2%
C 3011
 
4.7%
s 2973
 
4.7%
g 2031
 
3.2%
c 2017
 
3.2%
Other values (13) 19793
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63910
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 9972
15.6%
a 5983
 
9.4%
t 5150
 
8.1%
o 5013
 
7.8%
n 4019
 
6.3%
r 3948
 
6.2%
C 3011
 
4.7%
s 2973
 
4.7%
g 2031
 
3.2%
c 2017
 
3.2%
Other values (13) 19793
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63910
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 9972
15.6%
a 5983
 
9.4%
t 5150
 
8.1%
o 5013
 
7.8%
n 4019
 
6.3%
r 3948
 
6.2%
C 3011
 
4.7%
s 2973
 
4.7%
g 2031
 
3.2%
c 2017
 
3.2%
Other values (13) 19793
31.0%

Average_Temperature_C
Real number (ℝ)

Distinct3677
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.241299
Minimum-4.99
Maximum35
Zeros0
Zeros (%)0.0%
Negative1185
Negative (%)11.8%
Memory size78.2 KiB
2024-10-19T18:26:36.015283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4.99
5-th percentile-2.8105
Q15.43
median15.175
Q325.34
95-th percentile32.9605
Maximum35
Range39.99
Interquartile range (IQR)19.91

Descriptive statistics

Standard deviation11.466955
Coefficient of variation (CV)0.75236071
Kurtosis-1.1906732
Mean15.241299
Median Absolute Deviation (MAD)9.98
Skewness-0.011513118
Sum152412.99
Variance131.49105
MonotonicityNot monotonic
2024-10-19T18:26:36.335539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.41 9
 
0.1%
33.19 9
 
0.1%
20.35 9
 
0.1%
18.13 8
 
0.1%
0.15 8
 
0.1%
3.27 8
 
0.1%
25.43 8
 
0.1%
15.09 8
 
0.1%
11.75 8
 
0.1%
31.89 8
 
0.1%
Other values (3667) 9917
99.2%
ValueCountFrequency (%)
-4.99 1
 
< 0.1%
-4.98 5
0.1%
-4.97 2
 
< 0.1%
-4.96 1
 
< 0.1%
-4.95 2
 
< 0.1%
-4.94 2
 
< 0.1%
-4.93 2
 
< 0.1%
-4.92 1
 
< 0.1%
-4.91 3
< 0.1%
-4.9 1
 
< 0.1%
ValueCountFrequency (%)
35 1
 
< 0.1%
34.99 2
 
< 0.1%
34.98 4
< 0.1%
34.97 4
< 0.1%
34.94 1
 
< 0.1%
34.93 1
 
< 0.1%
34.92 3
< 0.1%
34.91 1
 
< 0.1%
34.9 2
 
< 0.1%
34.89 5
0.1%

Total_Precipitation_mm
Real number (ℝ)

Distinct9784
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1611.6638
Minimum200.15
Maximum2999.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-10-19T18:26:36.650854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum200.15
5-th percentile348.866
Q1925.6975
median1611.16
Q32306.9975
95-th percentile2870.141
Maximum2999.67
Range2799.52
Interquartile range (IQR)1381.3

Descriptive statistics

Standard deviation805.01681
Coefficient of variation (CV)0.49949425
Kurtosis-1.1876259
Mean1611.6638
Median Absolute Deviation (MAD)690.56
Skewness-0.006013876
Sum16116638
Variance648052.07
MonotonicityNot monotonic
2024-10-19T18:26:36.957707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1377.47 3
 
< 0.1%
2624.98 2
 
< 0.1%
2642.21 2
 
< 0.1%
2855.34 2
 
< 0.1%
679.97 2
 
< 0.1%
1505.05 2
 
< 0.1%
984.19 2
 
< 0.1%
1972.87 2
 
< 0.1%
2909.45 2
 
< 0.1%
865.03 2
 
< 0.1%
Other values (9774) 9979
99.8%
ValueCountFrequency (%)
200.15 1
< 0.1%
200.17 1
< 0.1%
200.44 1
< 0.1%
200.45 1
< 0.1%
200.46 1
< 0.1%
201.27 1
< 0.1%
201.64 1
< 0.1%
202.45 1
< 0.1%
202.76 1
< 0.1%
202.81 1
< 0.1%
ValueCountFrequency (%)
2999.67 1
< 0.1%
2999.19 1
< 0.1%
2999.1 1
< 0.1%
2998.88 1
< 0.1%
2998.72 1
< 0.1%
2998.61 1
< 0.1%
2998.47 1
< 0.1%
2998.24 1
< 0.1%
2998.22 1
< 0.1%
2997.84 1
< 0.1%

CO2_Emissions_MT
Real number (ℝ)

Distinct2852
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.246608
Minimum0.5
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-10-19T18:26:37.306597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1.95
Q17.76
median15.2
Q322.82
95-th percentile28.6
Maximum30
Range29.5
Interquartile range (IQR)15.06

Descriptive statistics

Standard deviation8.5894229
Coefficient of variation (CV)0.56336615
Kurtosis-1.2097611
Mean15.246608
Median Absolute Deviation (MAD)7.53
Skewness0.0099345449
Sum152466.08
Variance73.778186
MonotonicityNot monotonic
2024-10-19T18:26:37.617590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.23 11
 
0.1%
5.34 10
 
0.1%
27.77 10
 
0.1%
17.71 10
 
0.1%
19.85 10
 
0.1%
22.76 10
 
0.1%
1.06 10
 
0.1%
1.14 10
 
0.1%
4.89 10
 
0.1%
11.81 10
 
0.1%
Other values (2842) 9899
99.0%
ValueCountFrequency (%)
0.5 2
< 0.1%
0.51 4
< 0.1%
0.52 4
< 0.1%
0.53 4
< 0.1%
0.54 2
< 0.1%
0.55 4
< 0.1%
0.56 3
< 0.1%
0.57 2
< 0.1%
0.58 1
 
< 0.1%
0.59 3
< 0.1%
ValueCountFrequency (%)
30 2
 
< 0.1%
29.99 1
 
< 0.1%
29.98 5
0.1%
29.97 8
0.1%
29.96 3
 
< 0.1%
29.95 4
< 0.1%
29.94 4
< 0.1%
29.93 5
0.1%
29.92 3
 
< 0.1%
29.91 4
< 0.1%

Crop_Yield_MT_per_HA
Real number (ℝ)

High correlation 

Distinct850
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2400169
Minimum0.45
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-10-19T18:26:37.941881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.45
5-th percentile0.765
Q11.449
median2.17
Q32.93
95-th percentile4
Maximum5
Range4.55
Interquartile range (IQR)1.481

Descriptive statistics

Standard deviation0.99834152
Coefficient of variation (CV)0.44568481
Kurtosis-0.5657602
Mean2.2400169
Median Absolute Deviation (MAD)0.739
Skewness0.35731073
Sum22400.169
Variance0.99668578
MonotonicityNot monotonic
2024-10-19T18:26:38.386313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.53 44
 
0.4%
2.16 39
 
0.4%
1.8 37
 
0.4%
2.52 36
 
0.4%
0.9 36
 
0.4%
1.98 36
 
0.4%
2.97 35
 
0.4%
2.43 35
 
0.4%
2.7 34
 
0.3%
1.845 33
 
0.3%
Other values (840) 9635
96.4%
ValueCountFrequency (%)
0.45 5
0.1%
0.459 5
0.1%
0.468 7
0.1%
0.477 7
0.1%
0.486 7
0.1%
0.5 1
 
< 0.1%
0.504 7
0.1%
0.51 5
0.1%
0.513 5
0.1%
0.52 4
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
4.99 3
< 0.1%
4.98 1
 
< 0.1%
4.97 5
0.1%
4.96 4
< 0.1%
4.95 5
0.1%
4.94 4
< 0.1%
4.92 2
 
< 0.1%
4.91 2
 
< 0.1%
4.9 3
< 0.1%

Extreme_Weather_Events
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9809
Minimum0
Maximum10
Zeros899
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-10-19T18:26:38.806467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.1658075
Coefficient of variation (CV)0.63558946
Kurtosis-1.223471
Mean4.9809
Median Absolute Deviation (MAD)3
Skewness0.011542808
Sum49809
Variance10.022337
MonotonicityNot monotonic
2024-10-19T18:26:39.171030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 957
9.6%
6 929
9.3%
9 917
9.2%
3 915
9.2%
5 914
9.1%
10 908
9.1%
2 899
9.0%
0 899
9.0%
4 895
8.9%
7 884
8.8%
ValueCountFrequency (%)
0 899
9.0%
1 957
9.6%
2 899
9.0%
3 915
9.2%
4 895
8.9%
5 914
9.1%
6 929
9.3%
7 884
8.8%
8 883
8.8%
9 917
9.2%
ValueCountFrequency (%)
10 908
9.1%
9 917
9.2%
8 883
8.8%
7 884
8.8%
6 929
9.3%
5 914
9.1%
4 895
8.9%
3 915
9.2%
2 899
9.0%
1 957
9.6%

Irrigation_Access_%
Real number (ℝ)

Distinct6003
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.248332
Minimum10.01
Maximum99.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-10-19T18:26:39.611377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10.01
5-th percentile14.69
Q132.6775
median55.175
Q377.5825
95-th percentile95.5705
Maximum99.99
Range89.98
Interquartile range (IQR)44.905

Descriptive statistics

Standard deviation25.988305
Coefficient of variation (CV)0.47039076
Kurtosis-1.1992482
Mean55.248332
Median Absolute Deviation (MAD)22.445
Skewness-0.0086593929
Sum552483.32
Variance675.39198
MonotonicityNot monotonic
2024-10-19T18:26:40.080190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79.29 7
 
0.1%
74.86 6
 
0.1%
19.24 6
 
0.1%
98.54 6
 
0.1%
80.44 6
 
0.1%
88.8 6
 
0.1%
95.29 6
 
0.1%
97.42 6
 
0.1%
52.43 6
 
0.1%
50.79 6
 
0.1%
Other values (5993) 9939
99.4%
ValueCountFrequency (%)
10.01 3
< 0.1%
10.04 1
 
< 0.1%
10.05 1
 
< 0.1%
10.06 3
< 0.1%
10.07 5
0.1%
10.1 1
 
< 0.1%
10.11 1
 
< 0.1%
10.12 1
 
< 0.1%
10.13 2
 
< 0.1%
10.16 1
 
< 0.1%
ValueCountFrequency (%)
99.99 1
 
< 0.1%
99.98 2
< 0.1%
99.97 2
< 0.1%
99.95 3
< 0.1%
99.94 2
< 0.1%
99.91 1
 
< 0.1%
99.9 1
 
< 0.1%
99.88 2
< 0.1%
99.87 2
< 0.1%
99.86 1
 
< 0.1%

Pesticide_Use_KG_per_HA
Real number (ℝ)

Distinct4343
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.955735
Minimum0
Maximum49.99
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-10-19T18:26:40.570300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.37
Q112.5275
median24.93
Q337.47
95-th percentile47.4905
Maximum49.99
Range49.99
Interquartile range (IQR)24.9425

Descriptive statistics

Standard deviation14.490962
Coefficient of variation (CV)0.58066662
Kurtosis-1.2062211
Mean24.955735
Median Absolute Deviation (MAD)12.5
Skewness-0.001523802
Sum249557.35
Variance209.98799
MonotonicityNot monotonic
2024-10-19T18:26:41.113616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.76 8
 
0.1%
44.69 8
 
0.1%
0.72 8
 
0.1%
2.78 8
 
0.1%
5.82 8
 
0.1%
3.61 8
 
0.1%
44.62 7
 
0.1%
20.73 7
 
0.1%
44.58 7
 
0.1%
17.24 7
 
0.1%
Other values (4333) 9924
99.2%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.03 4
< 0.1%
0.04 2
 
< 0.1%
0.05 1
 
< 0.1%
0.06 1
 
< 0.1%
0.07 5
0.1%
0.08 5
0.1%
0.09 1
 
< 0.1%
0.1 3
< 0.1%
0.11 1
 
< 0.1%
ValueCountFrequency (%)
49.99 3
< 0.1%
49.96 1
 
< 0.1%
49.95 3
< 0.1%
49.94 2
< 0.1%
49.93 1
 
< 0.1%
49.91 1
 
< 0.1%
49.9 1
 
< 0.1%
49.89 3
< 0.1%
49.88 2
< 0.1%
49.87 3
< 0.1%

Fertilizer_Use_KG_per_HA
Real number (ℝ)

Distinct6314
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.973708
Minimum0.01
Maximum99.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-10-19T18:26:42.450414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile5.359
Q125.39
median49.635
Q374.825
95-th percentile94.7925
Maximum99.99
Range99.98
Interquartile range (IQR)49.435

Descriptive statistics

Standard deviation28.711027
Coefficient of variation (CV)0.57452265
Kurtosis-1.1939141
Mean49.973708
Median Absolute Deviation (MAD)24.72
Skewness0.013391105
Sum499737.08
Variance824.32307
MonotonicityNot monotonic
2024-10-19T18:26:42.893024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.07 6
 
0.1%
77.99 6
 
0.1%
93.14 6
 
0.1%
65.33 6
 
0.1%
72.31 6
 
0.1%
93.93 6
 
0.1%
94.79 5
 
0.1%
86.47 5
 
0.1%
95.99 5
 
0.1%
57.22 5
 
0.1%
Other values (6304) 9944
99.4%
ValueCountFrequency (%)
0.01 1
< 0.1%
0.03 1
< 0.1%
0.05 1
< 0.1%
0.07 1
< 0.1%
0.08 1
< 0.1%
0.09 1
< 0.1%
0.1 1
< 0.1%
0.12 1
< 0.1%
0.14 1
< 0.1%
0.15 2
< 0.1%
ValueCountFrequency (%)
99.99 1
< 0.1%
99.98 1
< 0.1%
99.97 2
< 0.1%
99.91 1
< 0.1%
99.9 1
< 0.1%
99.89 2
< 0.1%
99.88 2
< 0.1%
99.87 1
< 0.1%
99.86 2
< 0.1%
99.85 2
< 0.1%

Soil_Health_Index
Real number (ℝ)

Distinct5318
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.901278
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-10-19T18:26:43.214510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33.58
Q147.235
median64.65
Q382.4725
95-th percentile96.59
Maximum100
Range70
Interquartile range (IQR)35.2375

Descriptive statistics

Standard deviation20.195882
Coefficient of variation (CV)0.3111785
Kurtosis-1.1959077
Mean64.901278
Median Absolute Deviation (MAD)17.62
Skewness0.01119563
Sum649012.78
Variance407.87366
MonotonicityNot monotonic
2024-10-19T18:26:43.521328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79.38 8
 
0.1%
63.03 7
 
0.1%
64.29 6
 
0.1%
63.62 6
 
0.1%
31.44 6
 
0.1%
98.05 6
 
0.1%
84.32 6
 
0.1%
98.97 6
 
0.1%
97.38 6
 
0.1%
61.44 6
 
0.1%
Other values (5308) 9937
99.4%
ValueCountFrequency (%)
30 1
< 0.1%
30.02 1
< 0.1%
30.03 1
< 0.1%
30.04 2
< 0.1%
30.05 1
< 0.1%
30.06 2
< 0.1%
30.07 2
< 0.1%
30.08 1
< 0.1%
30.09 1
< 0.1%
30.1 2
< 0.1%
ValueCountFrequency (%)
100 1
 
< 0.1%
99.99 1
 
< 0.1%
99.98 4
< 0.1%
99.97 2
< 0.1%
99.96 2
< 0.1%
99.95 2
< 0.1%
99.94 3
< 0.1%
99.92 2
< 0.1%
99.9 1
 
< 0.1%
99.89 1
 
< 0.1%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Water Management
2049 
No Adaptation
2024 
Drought-resistant Crops
1995 
Organic Farming
1975 
Crop Rotation
1957 

Length

Max length23
Median length16
Mean length16.0047
Min length13

Characters and Unicode

Total characters160047
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWater Management
2nd rowCrop Rotation
3rd rowWater Management
4th rowNo Adaptation
5th rowCrop Rotation

Common Values

ValueCountFrequency (%)
Water Management 2049
20.5%
No Adaptation 2024
20.2%
Drought-resistant Crops 1995
20.0%
Organic Farming 1975
19.8%
Crop Rotation 1957
19.6%

Length

2024-10-19T18:26:43.839929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-19T18:26:44.141303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
water 2049
10.2%
management 2049
10.2%
no 2024
10.1%
adaptation 2024
10.1%
drought-resistant 1995
10.0%
crops 1995
10.0%
organic 1975
9.9%
farming 1975
9.9%
crop 1957
9.8%
rotation 1957
9.8%

Most occurring characters

ValueCountFrequency (%)
a 18097
11.3%
t 18045
11.3%
n 14024
 
8.8%
r 13941
 
8.7%
o 13909
 
8.7%
10000
 
6.2%
i 9926
 
6.2%
e 8142
 
5.1%
g 7994
 
5.0%
s 5985
 
3.7%
Other values (16) 39984
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 160047
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 18097
11.3%
t 18045
11.3%
n 14024
 
8.8%
r 13941
 
8.7%
o 13909
 
8.7%
10000
 
6.2%
i 9926
 
6.2%
e 8142
 
5.1%
g 7994
 
5.0%
s 5985
 
3.7%
Other values (16) 39984
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 160047
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 18097
11.3%
t 18045
11.3%
n 14024
 
8.8%
r 13941
 
8.7%
o 13909
 
8.7%
10000
 
6.2%
i 9926
 
6.2%
e 8142
 
5.1%
g 7994
 
5.0%
s 5985
 
3.7%
Other values (16) 39984
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 160047
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 18097
11.3%
t 18045
11.3%
n 14024
 
8.8%
r 13941
 
8.7%
o 13909
 
8.7%
10000
 
6.2%
i 9926
 
6.2%
e 8142
 
5.1%
g 7994
 
5.0%
s 5985
 
3.7%
Other values (16) 39984
25.0%

Economic_Impact_Million_USD
Real number (ℝ)

High correlation 

Distinct9631
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean674.26966
Minimum47.84
Maximum2346.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-10-19T18:26:44.440848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum47.84
5-th percentile169.627
Q1350.545
median583.92
Q3917.505
95-th percentile1486.191
Maximum2346.47
Range2298.63
Interquartile range (IQR)566.96

Descriptive statistics

Standard deviation414.59143
Coefficient of variation (CV)0.61487482
Kurtosis0.58777117
Mean674.26966
Median Absolute Deviation (MAD)267.37
Skewness0.96177136
Sum6742696.6
Variance171886.05
MonotonicityNot monotonic
2024-10-19T18:26:44.744106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
616.6 4
 
< 0.1%
1012.69 3
 
< 0.1%
447.71 3
 
< 0.1%
227.76 3
 
< 0.1%
643.72 3
 
< 0.1%
270.4 3
 
< 0.1%
254.47 3
 
< 0.1%
323.32 3
 
< 0.1%
304.07 3
 
< 0.1%
396.57 2
 
< 0.1%
Other values (9621) 9970
99.7%
ValueCountFrequency (%)
47.84 1
< 0.1%
53.76 1
< 0.1%
56.37 1
< 0.1%
59.17 1
< 0.1%
59.35 1
< 0.1%
59.44 1
< 0.1%
60.23 1
< 0.1%
60.64 1
< 0.1%
60.86 1
< 0.1%
61.79 1
< 0.1%
ValueCountFrequency (%)
2346.47 1
< 0.1%
2312.29 1
< 0.1%
2300 1
< 0.1%
2285.25 1
< 0.1%
2272.63 1
< 0.1%
2270.28 1
< 0.1%
2263.98 1
< 0.1%
2218.28 1
< 0.1%
2217.78 1
< 0.1%
2200.79 1
< 0.1%

Interactions

2024-10-19T18:26:29.534130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:25:54.414660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:25:58.545262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:01.590281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:04.697552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:07.757255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:12.018720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:15.311012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:19.122878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:22.064341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:25.821748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:29.802461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:25:54.854933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:25:58.829515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:01.883895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:04.983252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:08.148651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:12.455037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:15.570888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:19.396801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:22.344506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:26.252180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:30.082238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:25:55.285564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:25:59.115841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:02.163222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:05.268463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:08.565540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:12.928223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:16.718868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:19.674510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:22.624033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:26.643993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:30.350603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:25:55.702102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:25:59.394112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:02.479846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:05.549073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:08.965109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:13.194516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:16.995019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:19.946398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:22.892774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:27.034647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:30.635860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:25:56.110634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:25:59.664878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:02.755971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:05.826330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:09.382355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:13.453099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:17.260733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:20.222331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:23.239893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:27.467896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:30.895225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:25:56.527242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:25:59.930752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:03.051856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:06.091249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:09.760101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:13.704140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:17.508625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:20.481047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:23.576627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:27.834846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:31.152301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:25:56.947230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:00.205927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:03.329346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:06.360104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:10.141682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:13.974192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:17.763288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:20.746556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:23.957897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:28.169509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:31.400841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:25:57.322605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:00.477784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:03.592333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:06.637589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:10.473906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:14.228732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:18.059891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:21.012226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:24.302729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:28.437349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:31.668854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:25:57.714179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:00.749716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:03.876160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:06.912728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:10.829351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:14.486222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:18.322298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:21.281270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:24.644055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:28.716564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:31.929256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:25:57.985376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:01.021003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:04.138012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:07.175369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:11.244371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:14.739791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:18.577433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:21.535674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:25.017224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:28.984848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:32.192259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:25:58.281648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:01.322266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:04.434452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:07.480188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:11.638007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:15.048660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:18.859694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:21.804780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:25.432402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-19T18:26:29.270858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-10-19T18:26:45.005762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Adaptation_StrategiesAverage_Temperature_CCO2_Emissions_MTCountryCrop_TypeCrop_Yield_MT_per_HAEconomic_Impact_Million_USDExtreme_Weather_EventsFertilizer_Use_KG_per_HAIrrigation_Access_%Pesticide_Use_KG_per_HARegionSoil_Health_IndexTotal_Precipitation_mmYear
Adaptation_Strategies1.0000.0000.0000.0000.0150.0000.0000.0000.0120.0000.0000.0060.0000.0150.016
Average_Temperature_C0.0001.000-0.0030.0000.0000.2770.206-0.017-0.014-0.0130.0060.000-0.0110.008-0.005
CO2_Emissions_MT0.000-0.0031.0000.0000.000-0.082-0.0590.001-0.0200.0030.0150.0070.004-0.009-0.005
Country0.0000.0000.0001.0000.0130.0000.0090.0090.0000.0100.0000.9110.0000.0160.004
Crop_Type0.0150.0000.0000.0131.0000.0000.0140.0050.0030.0120.0070.0240.0100.0000.000
Crop_Yield_MT_per_HA0.0000.277-0.0820.0000.0001.0000.734-0.0070.007-0.001-0.0050.009-0.0070.0280.008
Economic_Impact_Million_USD0.0000.206-0.0590.0090.0140.7341.000-0.0070.0100.004-0.0090.000-0.0070.0190.007
Extreme_Weather_Events0.000-0.0170.0010.0090.005-0.007-0.0071.0000.015-0.0120.0100.0100.0160.005-0.003
Fertilizer_Use_KG_per_HA0.012-0.014-0.0200.0000.0030.0070.0100.0151.0000.008-0.0150.006-0.000-0.0260.013
Irrigation_Access_%0.000-0.0130.0030.0100.012-0.0010.004-0.0120.0081.000-0.0050.0000.002-0.0080.001
Pesticide_Use_KG_per_HA0.0000.0060.0150.0000.007-0.005-0.0090.010-0.015-0.0051.0000.0090.0130.011-0.004
Region0.0060.0000.0070.9110.0240.0090.0000.0100.0060.0000.0091.0000.0150.0000.013
Soil_Health_Index0.000-0.0110.0040.0000.010-0.007-0.0070.016-0.0000.0020.0130.0151.000-0.022-0.007
Total_Precipitation_mm0.0150.008-0.0090.0160.0000.0280.0190.005-0.026-0.0080.0110.000-0.0221.0000.007
Year0.016-0.005-0.0050.0040.0000.0080.007-0.0030.0130.001-0.0040.013-0.0070.0071.000

Missing values

2024-10-19T18:26:32.591038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-19T18:26:33.207203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

YearCountryRegionCrop_TypeAverage_Temperature_CTotal_Precipitation_mmCO2_Emissions_MTCrop_Yield_MT_per_HAExtreme_Weather_EventsIrrigation_Access_%Pesticide_Use_KG_per_HAFertilizer_Use_KG_per_HASoil_Health_IndexAdaptation_StrategiesEconomic_Impact_Million_USD
02001IndiaWest BengalCorn1.55447.0615.221.737814.5410.0814.7883.25Water Management808.13
12024ChinaNorthCorn3.232913.5729.821.737811.0533.0623.2554.02Crop Rotation616.22
22001FranceIle-de-FranceWheat21.111301.7425.751.719584.4227.4165.5367.78Water Management796.96
32001CanadaPrairiesCoffee27.851154.3613.913.890594.0614.3887.5891.39No Adaptation790.32
41998IndiaTamil NaduSugarcane2.191627.4811.811.080995.7544.3588.0849.61Crop Rotation401.72
52019USAMidwestCoffee17.19975.1310.732.180552.4526.0671.5697.32Water Management353.16
61997ArgentinaNortheastFruits23.461816.4127.701.611220.220.5628.7279.09Organic Farming480.61
72021AustraliaNew South WalesRice25.63786.177.773.270466.5313.4440.5063.16Drought-resistant Crops1367.97
82012IndiaPunjabWheat32.081233.106.102.990145.8710.0932.5442.31Water Management761.89
92018NigeriaNorth WestBarley21.23475.3225.740.765125.7444.3872.9284.21No Adaptation167.21
YearCountryRegionCrop_TypeAverage_Temperature_CTotal_Precipitation_mmCO2_Emissions_MTCrop_Yield_MT_per_HAExtreme_Weather_EventsIrrigation_Access_%Pesticide_Use_KG_per_HAFertilizer_Use_KG_per_HASoil_Health_IndexAdaptation_StrategiesEconomic_Impact_Million_USD
99902004ChinaCentralRice16.172571.781.564.530883.766.4054.7944.81No Adaptation981.05
99912016ChinaEastBarley-3.442997.5720.181.197699.9830.6587.8755.42Drought-resistant Crops181.28
99921995FranceGrand EstSoybeans3.82762.699.131.100989.833.4259.9988.75Organic Farming227.30
99931990FranceIle-de-FranceCoffee16.522102.2816.422.448051.3444.5479.0291.99No Adaptation523.60
99942011ChinaNorthCoffee15.262770.6413.653.010685.4543.005.6495.09Water Management1047.69
99952022FranceNouvelle-AquitaineCotton30.48685.9317.643.033927.5641.9610.9543.41No Adaptation1483.06
99961999AustraliaQueenslandSoybeans9.532560.3810.682.560477.025.4582.3259.39No Adaptation829.61
99972000ArgentinaPatagoniaCoffee31.92357.7626.011.1611078.5311.9426.0041.46Water Management155.99
99981996BrazilSoutheastSoybeans13.951549.5217.313.348242.6544.7125.0775.10Crop Rotation1613.90
99992015ChinaSouthCorn11.781676.255.343.710546.4148.2898.2759.38Water Management453.14